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Java API for TensorFlow protocol buffers.
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/protobuf/config.proto
package org.tensorflow.framework;
/**
*
* Defines a subgraph in another `GraphDef` as a set of feed points and nodes
* to be fetched or executed.
* Compare with the arguments to `Session::Run()`.
*
*
* Protobuf type {@code tensorflow.CallableOptions}
*/
public final class CallableOptions extends
com.google.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.CallableOptions)
CallableOptionsOrBuilder {
private static final long serialVersionUID = 0L;
// Use CallableOptions.newBuilder() to construct.
private CallableOptions(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private CallableOptions() {
feed_ = com.google.protobuf.LazyStringArrayList.EMPTY;
fetch_ = com.google.protobuf.LazyStringArrayList.EMPTY;
target_ = com.google.protobuf.LazyStringArrayList.EMPTY;
tensorConnection_ = java.util.Collections.emptyList();
fetchSkipSync_ = false;
}
@java.lang.Override
public final com.google.protobuf.UnknownFieldSet
getUnknownFields() {
return this.unknownFields;
}
private CallableOptions(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
this();
if (extensionRegistry == null) {
throw new java.lang.NullPointerException();
}
int mutable_bitField0_ = 0;
com.google.protobuf.UnknownFieldSet.Builder unknownFields =
com.google.protobuf.UnknownFieldSet.newBuilder();
try {
boolean done = false;
while (!done) {
int tag = input.readTag();
switch (tag) {
case 0:
done = true;
break;
default: {
if (!parseUnknownFieldProto3(
input, unknownFields, extensionRegistry, tag)) {
done = true;
}
break;
}
case 10: {
java.lang.String s = input.readStringRequireUtf8();
if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) {
feed_ = new com.google.protobuf.LazyStringArrayList();
mutable_bitField0_ |= 0x00000001;
}
feed_.add(s);
break;
}
case 18: {
java.lang.String s = input.readStringRequireUtf8();
if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) {
fetch_ = new com.google.protobuf.LazyStringArrayList();
mutable_bitField0_ |= 0x00000002;
}
fetch_.add(s);
break;
}
case 26: {
java.lang.String s = input.readStringRequireUtf8();
if (!((mutable_bitField0_ & 0x00000004) == 0x00000004)) {
target_ = new com.google.protobuf.LazyStringArrayList();
mutable_bitField0_ |= 0x00000004;
}
target_.add(s);
break;
}
case 34: {
org.tensorflow.framework.RunOptions.Builder subBuilder = null;
if (runOptions_ != null) {
subBuilder = runOptions_.toBuilder();
}
runOptions_ = input.readMessage(org.tensorflow.framework.RunOptions.parser(), extensionRegistry);
if (subBuilder != null) {
subBuilder.mergeFrom(runOptions_);
runOptions_ = subBuilder.buildPartial();
}
break;
}
case 42: {
if (!((mutable_bitField0_ & 0x00000010) == 0x00000010)) {
tensorConnection_ = new java.util.ArrayList();
mutable_bitField0_ |= 0x00000010;
}
tensorConnection_.add(
input.readMessage(org.tensorflow.framework.TensorConnection.parser(), extensionRegistry));
break;
}
case 50: {
if (!((mutable_bitField0_ & 0x00000020) == 0x00000020)) {
feedDevices_ = com.google.protobuf.MapField.newMapField(
FeedDevicesDefaultEntryHolder.defaultEntry);
mutable_bitField0_ |= 0x00000020;
}
com.google.protobuf.MapEntry
feedDevices__ = input.readMessage(
FeedDevicesDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry);
feedDevices_.getMutableMap().put(
feedDevices__.getKey(), feedDevices__.getValue());
break;
}
case 58: {
if (!((mutable_bitField0_ & 0x00000040) == 0x00000040)) {
fetchDevices_ = com.google.protobuf.MapField.newMapField(
FetchDevicesDefaultEntryHolder.defaultEntry);
mutable_bitField0_ |= 0x00000040;
}
com.google.protobuf.MapEntry
fetchDevices__ = input.readMessage(
FetchDevicesDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry);
fetchDevices_.getMutableMap().put(
fetchDevices__.getKey(), fetchDevices__.getValue());
break;
}
case 64: {
fetchSkipSync_ = input.readBool();
break;
}
}
}
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(this);
} catch (java.io.IOException e) {
throw new com.google.protobuf.InvalidProtocolBufferException(
e).setUnfinishedMessage(this);
} finally {
if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) {
feed_ = feed_.getUnmodifiableView();
}
if (((mutable_bitField0_ & 0x00000002) == 0x00000002)) {
fetch_ = fetch_.getUnmodifiableView();
}
if (((mutable_bitField0_ & 0x00000004) == 0x00000004)) {
target_ = target_.getUnmodifiableView();
}
if (((mutable_bitField0_ & 0x00000010) == 0x00000010)) {
tensorConnection_ = java.util.Collections.unmodifiableList(tensorConnection_);
}
this.unknownFields = unknownFields.build();
makeExtensionsImmutable();
}
}
public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_CallableOptions_descriptor;
}
@SuppressWarnings({"rawtypes"})
protected com.google.protobuf.MapField internalGetMapField(
int number) {
switch (number) {
case 6:
return internalGetFeedDevices();
case 7:
return internalGetFetchDevices();
default:
throw new RuntimeException(
"Invalid map field number: " + number);
}
}
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_CallableOptions_fieldAccessorTable
.ensureFieldAccessorsInitialized(
org.tensorflow.framework.CallableOptions.class, org.tensorflow.framework.CallableOptions.Builder.class);
}
private int bitField0_;
public static final int FEED_FIELD_NUMBER = 1;
private com.google.protobuf.LazyStringList feed_;
/**
*
* Tensors to be fed in the callable. Each feed is the name of a tensor.
*
*
* repeated string feed = 1;
*/
public com.google.protobuf.ProtocolStringList
getFeedList() {
return feed_;
}
/**
*
* Tensors to be fed in the callable. Each feed is the name of a tensor.
*
*
* repeated string feed = 1;
*/
public int getFeedCount() {
return feed_.size();
}
/**
*
* Tensors to be fed in the callable. Each feed is the name of a tensor.
*
*
* repeated string feed = 1;
*/
public java.lang.String getFeed(int index) {
return feed_.get(index);
}
/**
*
* Tensors to be fed in the callable. Each feed is the name of a tensor.
*
*
* repeated string feed = 1;
*/
public com.google.protobuf.ByteString
getFeedBytes(int index) {
return feed_.getByteString(index);
}
public static final int FETCH_FIELD_NUMBER = 2;
private com.google.protobuf.LazyStringList fetch_;
/**
*
* Fetches. A list of tensor names. The caller of the callable expects a
* tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
* order of specified fetches does not change the execution order.
*
*
* repeated string fetch = 2;
*/
public com.google.protobuf.ProtocolStringList
getFetchList() {
return fetch_;
}
/**
*
* Fetches. A list of tensor names. The caller of the callable expects a
* tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
* order of specified fetches does not change the execution order.
*
*
* repeated string fetch = 2;
*/
public int getFetchCount() {
return fetch_.size();
}
/**
*
* Fetches. A list of tensor names. The caller of the callable expects a
* tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
* order of specified fetches does not change the execution order.
*
*
* repeated string fetch = 2;
*/
public java.lang.String getFetch(int index) {
return fetch_.get(index);
}
/**
*
* Fetches. A list of tensor names. The caller of the callable expects a
* tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
* order of specified fetches does not change the execution order.
*
*
* repeated string fetch = 2;
*/
public com.google.protobuf.ByteString
getFetchBytes(int index) {
return fetch_.getByteString(index);
}
public static final int TARGET_FIELD_NUMBER = 3;
private com.google.protobuf.LazyStringList target_;
/**
*
* Target Nodes. A list of node names. The named nodes will be run by the
* callable but their outputs will not be returned.
*
*
* repeated string target = 3;
*/
public com.google.protobuf.ProtocolStringList
getTargetList() {
return target_;
}
/**
*
* Target Nodes. A list of node names. The named nodes will be run by the
* callable but their outputs will not be returned.
*
*
* repeated string target = 3;
*/
public int getTargetCount() {
return target_.size();
}
/**
*
* Target Nodes. A list of node names. The named nodes will be run by the
* callable but their outputs will not be returned.
*
*
* repeated string target = 3;
*/
public java.lang.String getTarget(int index) {
return target_.get(index);
}
/**
*
* Target Nodes. A list of node names. The named nodes will be run by the
* callable but their outputs will not be returned.
*
*
* repeated string target = 3;
*/
public com.google.protobuf.ByteString
getTargetBytes(int index) {
return target_.getByteString(index);
}
public static final int RUN_OPTIONS_FIELD_NUMBER = 4;
private org.tensorflow.framework.RunOptions runOptions_;
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
public boolean hasRunOptions() {
return runOptions_ != null;
}
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
public org.tensorflow.framework.RunOptions getRunOptions() {
return runOptions_ == null ? org.tensorflow.framework.RunOptions.getDefaultInstance() : runOptions_;
}
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
public org.tensorflow.framework.RunOptionsOrBuilder getRunOptionsOrBuilder() {
return getRunOptions();
}
public static final int TENSOR_CONNECTION_FIELD_NUMBER = 5;
private java.util.List tensorConnection_;
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public java.util.List getTensorConnectionList() {
return tensorConnection_;
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public java.util.List
getTensorConnectionOrBuilderList() {
return tensorConnection_;
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public int getTensorConnectionCount() {
return tensorConnection_.size();
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public org.tensorflow.framework.TensorConnection getTensorConnection(int index) {
return tensorConnection_.get(index);
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public org.tensorflow.framework.TensorConnectionOrBuilder getTensorConnectionOrBuilder(
int index) {
return tensorConnection_.get(index);
}
public static final int FEED_DEVICES_FIELD_NUMBER = 6;
private static final class FeedDevicesDefaultEntryHolder {
static final com.google.protobuf.MapEntry<
java.lang.String, java.lang.String> defaultEntry =
com.google.protobuf.MapEntry
.newDefaultInstance(
org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_CallableOptions_FeedDevicesEntry_descriptor,
com.google.protobuf.WireFormat.FieldType.STRING,
"",
com.google.protobuf.WireFormat.FieldType.STRING,
"");
}
private com.google.protobuf.MapField<
java.lang.String, java.lang.String> feedDevices_;
private com.google.protobuf.MapField
internalGetFeedDevices() {
if (feedDevices_ == null) {
return com.google.protobuf.MapField.emptyMapField(
FeedDevicesDefaultEntryHolder.defaultEntry);
}
return feedDevices_;
}
public int getFeedDevicesCount() {
return internalGetFeedDevices().getMap().size();
}
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
public boolean containsFeedDevices(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
return internalGetFeedDevices().getMap().containsKey(key);
}
/**
* Use {@link #getFeedDevicesMap()} instead.
*/
@java.lang.Deprecated
public java.util.Map getFeedDevices() {
return getFeedDevicesMap();
}
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
public java.util.Map getFeedDevicesMap() {
return internalGetFeedDevices().getMap();
}
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
public java.lang.String getFeedDevicesOrDefault(
java.lang.String key,
java.lang.String defaultValue) {
if (key == null) { throw new java.lang.NullPointerException(); }
java.util.Map map =
internalGetFeedDevices().getMap();
return map.containsKey(key) ? map.get(key) : defaultValue;
}
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
public java.lang.String getFeedDevicesOrThrow(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
java.util.Map map =
internalGetFeedDevices().getMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return map.get(key);
}
public static final int FETCH_DEVICES_FIELD_NUMBER = 7;
private static final class FetchDevicesDefaultEntryHolder {
static final com.google.protobuf.MapEntry<
java.lang.String, java.lang.String> defaultEntry =
com.google.protobuf.MapEntry
.newDefaultInstance(
org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_CallableOptions_FetchDevicesEntry_descriptor,
com.google.protobuf.WireFormat.FieldType.STRING,
"",
com.google.protobuf.WireFormat.FieldType.STRING,
"");
}
private com.google.protobuf.MapField<
java.lang.String, java.lang.String> fetchDevices_;
private com.google.protobuf.MapField
internalGetFetchDevices() {
if (fetchDevices_ == null) {
return com.google.protobuf.MapField.emptyMapField(
FetchDevicesDefaultEntryHolder.defaultEntry);
}
return fetchDevices_;
}
public int getFetchDevicesCount() {
return internalGetFetchDevices().getMap().size();
}
/**
* map<string, string> fetch_devices = 7;
*/
public boolean containsFetchDevices(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
return internalGetFetchDevices().getMap().containsKey(key);
}
/**
* Use {@link #getFetchDevicesMap()} instead.
*/
@java.lang.Deprecated
public java.util.Map getFetchDevices() {
return getFetchDevicesMap();
}
/**
* map<string, string> fetch_devices = 7;
*/
public java.util.Map getFetchDevicesMap() {
return internalGetFetchDevices().getMap();
}
/**
* map<string, string> fetch_devices = 7;
*/
public java.lang.String getFetchDevicesOrDefault(
java.lang.String key,
java.lang.String defaultValue) {
if (key == null) { throw new java.lang.NullPointerException(); }
java.util.Map map =
internalGetFetchDevices().getMap();
return map.containsKey(key) ? map.get(key) : defaultValue;
}
/**
* map<string, string> fetch_devices = 7;
*/
public java.lang.String getFetchDevicesOrThrow(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
java.util.Map map =
internalGetFetchDevices().getMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return map.get(key);
}
public static final int FETCH_SKIP_SYNC_FIELD_NUMBER = 8;
private boolean fetchSkipSync_;
/**
*
* By default, RunCallable() will synchronize the GPU stream before returning
* fetched tensors on a GPU device, to ensure that the values in those tensors
* have been produced. This simplifies interacting with the tensors, but
* potentially incurs a performance hit.
* If this options is set to true, the caller is responsible for ensuring
* that the values in the fetched tensors have been produced before they are
* used. The caller can do this by invoking `Device::Sync()` on the underlying
* device(s), or by feeding the tensors back to the same Session using
* `feed_devices` with the same corresponding device name.
*
*
* bool fetch_skip_sync = 8;
*/
public boolean getFetchSkipSync() {
return fetchSkipSync_;
}
private byte memoizedIsInitialized = -1;
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
public void writeTo(com.google.protobuf.CodedOutputStream output)
throws java.io.IOException {
for (int i = 0; i < feed_.size(); i++) {
com.google.protobuf.GeneratedMessageV3.writeString(output, 1, feed_.getRaw(i));
}
for (int i = 0; i < fetch_.size(); i++) {
com.google.protobuf.GeneratedMessageV3.writeString(output, 2, fetch_.getRaw(i));
}
for (int i = 0; i < target_.size(); i++) {
com.google.protobuf.GeneratedMessageV3.writeString(output, 3, target_.getRaw(i));
}
if (runOptions_ != null) {
output.writeMessage(4, getRunOptions());
}
for (int i = 0; i < tensorConnection_.size(); i++) {
output.writeMessage(5, tensorConnection_.get(i));
}
com.google.protobuf.GeneratedMessageV3
.serializeStringMapTo(
output,
internalGetFeedDevices(),
FeedDevicesDefaultEntryHolder.defaultEntry,
6);
com.google.protobuf.GeneratedMessageV3
.serializeStringMapTo(
output,
internalGetFetchDevices(),
FetchDevicesDefaultEntryHolder.defaultEntry,
7);
if (fetchSkipSync_ != false) {
output.writeBool(8, fetchSkipSync_);
}
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*/
public Builder addTarget(
java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
ensureTargetIsMutable();
target_.add(value);
onChanged();
return this;
}
/**
*
* Target Nodes. A list of node names. The named nodes will be run by the
* callable but their outputs will not be returned.
*
*
* repeated string target = 3;
*/
public Builder addAllTarget(
java.lang.Iterable values) {
ensureTargetIsMutable();
com.google.protobuf.AbstractMessageLite.Builder.addAll(
values, target_);
onChanged();
return this;
}
/**
*
* Target Nodes. A list of node names. The named nodes will be run by the
* callable but their outputs will not be returned.
*
*
* repeated string target = 3;
*/
public Builder clearTarget() {
target_ = com.google.protobuf.LazyStringArrayList.EMPTY;
bitField0_ = (bitField0_ & ~0x00000004);
onChanged();
return this;
}
/**
*
* Target Nodes. A list of node names. The named nodes will be run by the
* callable but their outputs will not be returned.
*
*
* repeated string target = 3;
*/
public Builder addTargetBytes(
com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
ensureTargetIsMutable();
target_.add(value);
onChanged();
return this;
}
private org.tensorflow.framework.RunOptions runOptions_ = null;
private com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.RunOptions, org.tensorflow.framework.RunOptions.Builder, org.tensorflow.framework.RunOptionsOrBuilder> runOptionsBuilder_;
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
public boolean hasRunOptions() {
return runOptionsBuilder_ != null || runOptions_ != null;
}
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
public org.tensorflow.framework.RunOptions getRunOptions() {
if (runOptionsBuilder_ == null) {
return runOptions_ == null ? org.tensorflow.framework.RunOptions.getDefaultInstance() : runOptions_;
} else {
return runOptionsBuilder_.getMessage();
}
}
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
public Builder setRunOptions(org.tensorflow.framework.RunOptions value) {
if (runOptionsBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
runOptions_ = value;
onChanged();
} else {
runOptionsBuilder_.setMessage(value);
}
return this;
}
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
public Builder setRunOptions(
org.tensorflow.framework.RunOptions.Builder builderForValue) {
if (runOptionsBuilder_ == null) {
runOptions_ = builderForValue.build();
onChanged();
} else {
runOptionsBuilder_.setMessage(builderForValue.build());
}
return this;
}
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
public Builder mergeRunOptions(org.tensorflow.framework.RunOptions value) {
if (runOptionsBuilder_ == null) {
if (runOptions_ != null) {
runOptions_ =
org.tensorflow.framework.RunOptions.newBuilder(runOptions_).mergeFrom(value).buildPartial();
} else {
runOptions_ = value;
}
onChanged();
} else {
runOptionsBuilder_.mergeFrom(value);
}
return this;
}
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
public Builder clearRunOptions() {
if (runOptionsBuilder_ == null) {
runOptions_ = null;
onChanged();
} else {
runOptions_ = null;
runOptionsBuilder_ = null;
}
return this;
}
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
public org.tensorflow.framework.RunOptions.Builder getRunOptionsBuilder() {
onChanged();
return getRunOptionsFieldBuilder().getBuilder();
}
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
public org.tensorflow.framework.RunOptionsOrBuilder getRunOptionsOrBuilder() {
if (runOptionsBuilder_ != null) {
return runOptionsBuilder_.getMessageOrBuilder();
} else {
return runOptions_ == null ?
org.tensorflow.framework.RunOptions.getDefaultInstance() : runOptions_;
}
}
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
private com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.RunOptions, org.tensorflow.framework.RunOptions.Builder, org.tensorflow.framework.RunOptionsOrBuilder>
getRunOptionsFieldBuilder() {
if (runOptionsBuilder_ == null) {
runOptionsBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.RunOptions, org.tensorflow.framework.RunOptions.Builder, org.tensorflow.framework.RunOptionsOrBuilder>(
getRunOptions(),
getParentForChildren(),
isClean());
runOptions_ = null;
}
return runOptionsBuilder_;
}
private java.util.List tensorConnection_ =
java.util.Collections.emptyList();
private void ensureTensorConnectionIsMutable() {
if (!((bitField0_ & 0x00000010) == 0x00000010)) {
tensorConnection_ = new java.util.ArrayList(tensorConnection_);
bitField0_ |= 0x00000010;
}
}
private com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.TensorConnection, org.tensorflow.framework.TensorConnection.Builder, org.tensorflow.framework.TensorConnectionOrBuilder> tensorConnectionBuilder_;
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public java.util.List getTensorConnectionList() {
if (tensorConnectionBuilder_ == null) {
return java.util.Collections.unmodifiableList(tensorConnection_);
} else {
return tensorConnectionBuilder_.getMessageList();
}
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public int getTensorConnectionCount() {
if (tensorConnectionBuilder_ == null) {
return tensorConnection_.size();
} else {
return tensorConnectionBuilder_.getCount();
}
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public org.tensorflow.framework.TensorConnection getTensorConnection(int index) {
if (tensorConnectionBuilder_ == null) {
return tensorConnection_.get(index);
} else {
return tensorConnectionBuilder_.getMessage(index);
}
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public Builder setTensorConnection(
int index, org.tensorflow.framework.TensorConnection value) {
if (tensorConnectionBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureTensorConnectionIsMutable();
tensorConnection_.set(index, value);
onChanged();
} else {
tensorConnectionBuilder_.setMessage(index, value);
}
return this;
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public Builder setTensorConnection(
int index, org.tensorflow.framework.TensorConnection.Builder builderForValue) {
if (tensorConnectionBuilder_ == null) {
ensureTensorConnectionIsMutable();
tensorConnection_.set(index, builderForValue.build());
onChanged();
} else {
tensorConnectionBuilder_.setMessage(index, builderForValue.build());
}
return this;
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public Builder addTensorConnection(org.tensorflow.framework.TensorConnection value) {
if (tensorConnectionBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureTensorConnectionIsMutable();
tensorConnection_.add(value);
onChanged();
} else {
tensorConnectionBuilder_.addMessage(value);
}
return this;
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public Builder addTensorConnection(
int index, org.tensorflow.framework.TensorConnection value) {
if (tensorConnectionBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureTensorConnectionIsMutable();
tensorConnection_.add(index, value);
onChanged();
} else {
tensorConnectionBuilder_.addMessage(index, value);
}
return this;
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public Builder addTensorConnection(
org.tensorflow.framework.TensorConnection.Builder builderForValue) {
if (tensorConnectionBuilder_ == null) {
ensureTensorConnectionIsMutable();
tensorConnection_.add(builderForValue.build());
onChanged();
} else {
tensorConnectionBuilder_.addMessage(builderForValue.build());
}
return this;
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public Builder addTensorConnection(
int index, org.tensorflow.framework.TensorConnection.Builder builderForValue) {
if (tensorConnectionBuilder_ == null) {
ensureTensorConnectionIsMutable();
tensorConnection_.add(index, builderForValue.build());
onChanged();
} else {
tensorConnectionBuilder_.addMessage(index, builderForValue.build());
}
return this;
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public Builder addAllTensorConnection(
java.lang.Iterable values) {
if (tensorConnectionBuilder_ == null) {
ensureTensorConnectionIsMutable();
com.google.protobuf.AbstractMessageLite.Builder.addAll(
values, tensorConnection_);
onChanged();
} else {
tensorConnectionBuilder_.addAllMessages(values);
}
return this;
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public Builder clearTensorConnection() {
if (tensorConnectionBuilder_ == null) {
tensorConnection_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000010);
onChanged();
} else {
tensorConnectionBuilder_.clear();
}
return this;
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public Builder removeTensorConnection(int index) {
if (tensorConnectionBuilder_ == null) {
ensureTensorConnectionIsMutable();
tensorConnection_.remove(index);
onChanged();
} else {
tensorConnectionBuilder_.remove(index);
}
return this;
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public org.tensorflow.framework.TensorConnection.Builder getTensorConnectionBuilder(
int index) {
return getTensorConnectionFieldBuilder().getBuilder(index);
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public org.tensorflow.framework.TensorConnectionOrBuilder getTensorConnectionOrBuilder(
int index) {
if (tensorConnectionBuilder_ == null) {
return tensorConnection_.get(index); } else {
return tensorConnectionBuilder_.getMessageOrBuilder(index);
}
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public java.util.List
getTensorConnectionOrBuilderList() {
if (tensorConnectionBuilder_ != null) {
return tensorConnectionBuilder_.getMessageOrBuilderList();
} else {
return java.util.Collections.unmodifiableList(tensorConnection_);
}
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public org.tensorflow.framework.TensorConnection.Builder addTensorConnectionBuilder() {
return getTensorConnectionFieldBuilder().addBuilder(
org.tensorflow.framework.TensorConnection.getDefaultInstance());
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public org.tensorflow.framework.TensorConnection.Builder addTensorConnectionBuilder(
int index) {
return getTensorConnectionFieldBuilder().addBuilder(
index, org.tensorflow.framework.TensorConnection.getDefaultInstance());
}
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
public java.util.List
getTensorConnectionBuilderList() {
return getTensorConnectionFieldBuilder().getBuilderList();
}
private com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.TensorConnection, org.tensorflow.framework.TensorConnection.Builder, org.tensorflow.framework.TensorConnectionOrBuilder>
getTensorConnectionFieldBuilder() {
if (tensorConnectionBuilder_ == null) {
tensorConnectionBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.TensorConnection, org.tensorflow.framework.TensorConnection.Builder, org.tensorflow.framework.TensorConnectionOrBuilder>(
tensorConnection_,
((bitField0_ & 0x00000010) == 0x00000010),
getParentForChildren(),
isClean());
tensorConnection_ = null;
}
return tensorConnectionBuilder_;
}
private com.google.protobuf.MapField<
java.lang.String, java.lang.String> feedDevices_;
private com.google.protobuf.MapField
internalGetFeedDevices() {
if (feedDevices_ == null) {
return com.google.protobuf.MapField.emptyMapField(
FeedDevicesDefaultEntryHolder.defaultEntry);
}
return feedDevices_;
}
private com.google.protobuf.MapField
internalGetMutableFeedDevices() {
onChanged();;
if (feedDevices_ == null) {
feedDevices_ = com.google.protobuf.MapField.newMapField(
FeedDevicesDefaultEntryHolder.defaultEntry);
}
if (!feedDevices_.isMutable()) {
feedDevices_ = feedDevices_.copy();
}
return feedDevices_;
}
public int getFeedDevicesCount() {
return internalGetFeedDevices().getMap().size();
}
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
public boolean containsFeedDevices(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
return internalGetFeedDevices().getMap().containsKey(key);
}
/**
* Use {@link #getFeedDevicesMap()} instead.
*/
@java.lang.Deprecated
public java.util.Map getFeedDevices() {
return getFeedDevicesMap();
}
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
public java.util.Map getFeedDevicesMap() {
return internalGetFeedDevices().getMap();
}
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
public java.lang.String getFeedDevicesOrDefault(
java.lang.String key,
java.lang.String defaultValue) {
if (key == null) { throw new java.lang.NullPointerException(); }
java.util.Map map =
internalGetFeedDevices().getMap();
return map.containsKey(key) ? map.get(key) : defaultValue;
}
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
public java.lang.String getFeedDevicesOrThrow(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
java.util.Map map =
internalGetFeedDevices().getMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return map.get(key);
}
public Builder clearFeedDevices() {
internalGetMutableFeedDevices().getMutableMap()
.clear();
return this;
}
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
public Builder removeFeedDevices(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
internalGetMutableFeedDevices().getMutableMap()
.remove(key);
return this;
}
/**
* Use alternate mutation accessors instead.
*/
@java.lang.Deprecated
public java.util.Map
getMutableFeedDevices() {
return internalGetMutableFeedDevices().getMutableMap();
}
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
public Builder putFeedDevices(
java.lang.String key,
java.lang.String value) {
if (key == null) { throw new java.lang.NullPointerException(); }
if (value == null) { throw new java.lang.NullPointerException(); }
internalGetMutableFeedDevices().getMutableMap()
.put(key, value);
return this;
}
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
public Builder putAllFeedDevices(
java.util.Map values) {
internalGetMutableFeedDevices().getMutableMap()
.putAll(values);
return this;
}
private com.google.protobuf.MapField<
java.lang.String, java.lang.String> fetchDevices_;
private com.google.protobuf.MapField
internalGetFetchDevices() {
if (fetchDevices_ == null) {
return com.google.protobuf.MapField.emptyMapField(
FetchDevicesDefaultEntryHolder.defaultEntry);
}
return fetchDevices_;
}
private com.google.protobuf.MapField
internalGetMutableFetchDevices() {
onChanged();;
if (fetchDevices_ == null) {
fetchDevices_ = com.google.protobuf.MapField.newMapField(
FetchDevicesDefaultEntryHolder.defaultEntry);
}
if (!fetchDevices_.isMutable()) {
fetchDevices_ = fetchDevices_.copy();
}
return fetchDevices_;
}
public int getFetchDevicesCount() {
return internalGetFetchDevices().getMap().size();
}
/**
* map<string, string> fetch_devices = 7;
*/
public boolean containsFetchDevices(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
return internalGetFetchDevices().getMap().containsKey(key);
}
/**
* Use {@link #getFetchDevicesMap()} instead.
*/
@java.lang.Deprecated
public java.util.Map getFetchDevices() {
return getFetchDevicesMap();
}
/**
* map<string, string> fetch_devices = 7;
*/
public java.util.Map getFetchDevicesMap() {
return internalGetFetchDevices().getMap();
}
/**
* map<string, string> fetch_devices = 7;
*/
public java.lang.String getFetchDevicesOrDefault(
java.lang.String key,
java.lang.String defaultValue) {
if (key == null) { throw new java.lang.NullPointerException(); }
java.util.Map map =
internalGetFetchDevices().getMap();
return map.containsKey(key) ? map.get(key) : defaultValue;
}
/**
* map<string, string> fetch_devices = 7;
*/
public java.lang.String getFetchDevicesOrThrow(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
java.util.Map map =
internalGetFetchDevices().getMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return map.get(key);
}
public Builder clearFetchDevices() {
internalGetMutableFetchDevices().getMutableMap()
.clear();
return this;
}
/**
* map<string, string> fetch_devices = 7;
*/
public Builder removeFetchDevices(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
internalGetMutableFetchDevices().getMutableMap()
.remove(key);
return this;
}
/**
* Use alternate mutation accessors instead.
*/
@java.lang.Deprecated
public java.util.Map
getMutableFetchDevices() {
return internalGetMutableFetchDevices().getMutableMap();
}
/**
* map<string, string> fetch_devices = 7;
*/
public Builder putFetchDevices(
java.lang.String key,
java.lang.String value) {
if (key == null) { throw new java.lang.NullPointerException(); }
if (value == null) { throw new java.lang.NullPointerException(); }
internalGetMutableFetchDevices().getMutableMap()
.put(key, value);
return this;
}
/**
* map<string, string> fetch_devices = 7;
*/
public Builder putAllFetchDevices(
java.util.Map values) {
internalGetMutableFetchDevices().getMutableMap()
.putAll(values);
return this;
}
private boolean fetchSkipSync_ ;
/**
*
* By default, RunCallable() will synchronize the GPU stream before returning
* fetched tensors on a GPU device, to ensure that the values in those tensors
* have been produced. This simplifies interacting with the tensors, but
* potentially incurs a performance hit.
* If this options is set to true, the caller is responsible for ensuring
* that the values in the fetched tensors have been produced before they are
* used. The caller can do this by invoking `Device::Sync()` on the underlying
* device(s), or by feeding the tensors back to the same Session using
* `feed_devices` with the same corresponding device name.
*
*
* bool fetch_skip_sync = 8;
*/
public boolean getFetchSkipSync() {
return fetchSkipSync_;
}
/**
*
* By default, RunCallable() will synchronize the GPU stream before returning
* fetched tensors on a GPU device, to ensure that the values in those tensors
* have been produced. This simplifies interacting with the tensors, but
* potentially incurs a performance hit.
* If this options is set to true, the caller is responsible for ensuring
* that the values in the fetched tensors have been produced before they are
* used. The caller can do this by invoking `Device::Sync()` on the underlying
* device(s), or by feeding the tensors back to the same Session using
* `feed_devices` with the same corresponding device name.
*
*
* bool fetch_skip_sync = 8;
*/
public Builder setFetchSkipSync(boolean value) {
fetchSkipSync_ = value;
onChanged();
return this;
}
/**
*
* By default, RunCallable() will synchronize the GPU stream before returning
* fetched tensors on a GPU device, to ensure that the values in those tensors
* have been produced. This simplifies interacting with the tensors, but
* potentially incurs a performance hit.
* If this options is set to true, the caller is responsible for ensuring
* that the values in the fetched tensors have been produced before they are
* used. The caller can do this by invoking `Device::Sync()` on the underlying
* device(s), or by feeding the tensors back to the same Session using
* `feed_devices` with the same corresponding device name.
*
*
* bool fetch_skip_sync = 8;
*/
public Builder clearFetchSkipSync() {
fetchSkipSync_ = false;
onChanged();
return this;
}
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFieldsProto3(unknownFields);
}
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:tensorflow.CallableOptions)
}
// @@protoc_insertion_point(class_scope:tensorflow.CallableOptions)
private static final org.tensorflow.framework.CallableOptions DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.framework.CallableOptions();
}
public static org.tensorflow.framework.CallableOptions getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
PARSER = new com.google.protobuf.AbstractParser() {
public CallableOptions parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return new CallableOptions(input, extensionRegistry);
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
public org.tensorflow.framework.CallableOptions getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
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